Bibliographic record
Abstract
In The French Atlantic: Travels in Culture and History , Bill Marshall cites data from the 2000 US census indicating that “13 million Americans are believed to be of French descent,” and 1.6 million respondents to the census “declared that they spoke French at home, the third highest figure for a language other than English, after Spanish and Chinese” (Marshall 2009, 4). Nevertheless, the fact of French in North America (especially outside of Quebec) seems to be a perennial surprise to many. Historically, many of the French speakers in North America have been located in contested areas at the edges of the United States and of Canada: southern Louisiana, northern New England, and Maine, which bleeds into the territory formerly known as Acadia (now New Brunswick and Nova Scotia). Much of what Marshall says about the city of New Orleans holds true for other Francophone regions of North America: they are “zone[s] of cultural and racial miscegenation” that do not easily fit into the popular paradigms about the development of US and Canadian nationhood or culture (Marshall 2009, 219). Rather, they are akin to the borderland, described by Gloria Anzaldúa in her book Borderlands/La Frontera: The New Mestiza as “a vague and undetermined place created by emotional residue of an unnatural boundary,” a place “in a constant state of transition” (Anzaldúa 1987, 3). Within the Americas, such regions are of particular value for Comparative American Studies, which, as Florian Freitag notes in this volume, “offers an opportunity to consider regions, regional writing, and regionalism in contexts that transcend the national” (ch. 11, 201). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".